In [1]:
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#   LEARN FCN01 from FCN02
#

from __future__ import print_function
import argparse
import os

import numpy as np
import pickle
from keras import backend as K
from keras.callbacks import ModelCheckpoint
from keras.models import Model
from keras.layers import Input
from keras.layers import Conv2D, MaxPooling2D, UpSampling2D, Concatenate
from keras.layers import merge
from keras.optimizers import Adam, SGD, RMSprop
from keras.preprocessing.image import list_pictures, array_to_img

from image_ext import list_pictures_in_multidir, load_imgs_asarray, img_dice_coeff
from create_fcn import create_fcn01, create_fcn02

np.random.seed(2016)
/home/nakazawa_atsushi/anaconda3/envs/py3/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
Using TensorFlow backend.
/home/nakazawa_atsushi/anaconda3/envs/py3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6
  return f(*args, **kwds)
In [2]:
def dice_coef(y_true, y_pred):
    y_true = K.flatten(y_true)
    y_pred = K.flatten(y_pred)
    intersection = K.sum(y_true * y_pred)
    return (2.*intersection + 1) / (K.sum(y_true) + K.sum(y_pred) + 1)

def dice_coef_loss(y_true, y_pred):
    return -dice_coef(y_true, y_pred)
In [3]:
def load_fnames(paths):
    f = open(paths)
    data1 = f.read()
    f.close()
    lines = data1.split('\n')
    #print(len(lines))
    # 最終行は空行なので消す
    del(lines[len(lines)-1])
    #print(len(lines))
    return lines
In [4]:
def make_fnames(fnames,fpath,fpath_mask,mask_ext):
    fnames_img = [];
    fnames_mask= [];
    
    for i in range(len(fnames)):
        fnames_img.append(fpath + '/' + fnames[i]);
        fnames_mask.append(fpath_mask + '/' + mask_ext + fnames[i]);
        
    return [fnames_img,fnames_mask]
In [33]:
def get_center(im):
    im[im>0] = 1;
    xval = 0
    yval = 0
    npix = 0

    for x in range(0,im.shape[1]):
        xval += (x*sum(im[:,x]))
        npix += sum(im[:,x])
    
    for y in range(0,im.shape[0]):
        yval += (y*sum(im[y,:]))
    
    return [(xval+1)/(npix+1),(yval+1)/(npix+1)]
In [6]:
#
#  MAIN STARTS FROM HERE
#
if __name__ == '__main__':
    
    target_size = (224, 224)
    dpath_this = './'
    dname_checkpoints = 'checkpoints_fcn01'
    dname_checkpoints_fcn02 = 'checkpoints_fcn02'
    dname_outputs = 'outputs'
    fname_architecture = 'architecture.json'
    fname_weights = "model_weights_{epoch:02d}.h5"
    fname_stats = 'stats01.npz'
    dim_ordering = 'channels_first'
    fname_history = "history.pkl"

    # definision of mode, LEARN or TEST or SHOW_HISTORY
    mode = "LEARN"
    #mode = "SHOW_HISTORY"
    #mode = "TEST"

    # モデルを作成
    print('creating model fcn01 and fcn02...')
    model_fcn02 = create_fcn02(target_size)
    model_fcn01 = create_fcn01(target_size)
    
    if os.path.exists(dname_checkpoints) == 0:
        os.mkdir(dname_checkpoints)
creating model fcn01 and fcn02...
In [6]:
#
#   LEARNING MODE
#
if mode == "LEARN":
    # Read Learning Data
    fnames = load_fnames('data/list_train_01.txt')
    [fpaths_xs_train,fpaths_ys_train] = make_fnames(fnames,'data/img','data/mask','OperatorA_')

    X_train = load_imgs_asarray(fpaths_xs_train, grayscale=False, target_size=target_size,
                                dim_ordering=dim_ordering)
    Y_train = load_imgs_asarray(fpaths_ys_train, grayscale=True, target_size=target_size,
                                dim_ordering=dim_ordering) 

    # Read Validation Data
    fnames = load_fnames('data/list_valid_01.txt')
    [fpaths_xs_valid,fpaths_ys_valid] = make_fnames(fnames,'data/img','data/mask','OperatorA_')

    X_valid = load_imgs_asarray(fpaths_xs_valid, grayscale=False, target_size=target_size,
                                dim_ordering=dim_ordering)
    Y_valid = load_imgs_asarray(fpaths_ys_valid, grayscale=True, target_size=target_size,
                                dim_ordering=dim_ordering)     

    print('==> ' + str(len(X_train)) + ' training images loaded')
    print('==> ' + str(len(Y_train)) + ' training masks loaded')
    print('==> ' + str(len(X_valid)) + ' validation images loaded')
    print('==> ' + str(len(Y_valid)) + ' validation masks loaded')

    # 前処理
    print('computing mean and standard deviation...')
    mean = np.mean(X_train, axis=(0, 2, 3))
    std = np.std(X_train, axis=(0, 2, 3))
    print('==> mean: ' + str(mean))
    print('==> std : ' + str(std))

    print('saving mean and standard deviation to ' + fname_stats + '...')
    stats = {'mean': mean, 'std': std}
    np.savez(dname_checkpoints + '/' + fname_stats, **stats)
    print('==> done')

    print('globally normalizing data...')
    for i in range(3):
        X_train[:, i] = (X_train[:, i] - mean[i]) / std[i]
        X_valid[:, i] = (X_valid[:, i] - mean[i]) / std[i]
    Y_train /= 255
    Y_valid /= 255
    print('==> done')
==> 1452 training images loaded
==> 1452 training masks loaded
==> 527 validation images loaded
==> 527 validation masks loaded
computing mean and standard deviation...
==> mean: [130.65465  91.2685   76.63643]
==> std : [55.2817   43.990963 43.113483]
saving mean and standard deviation to stats01.npz...
==> done
globally normalizing data...
==> done
In [8]:
    # モデルに学習済のfcn02 Weightをロードする
    epoch = 200
    fname_weights = 'model_weights_%02d.h5'%(epoch)
    fpath_weights_fcn02 = os.path.join(dname_checkpoints_fcn02, fname_weights)
    model_fcn02.load_weights(fpath_weights_fcn02)
    print('==> done')

    # load weights from Learned U-NET
    layer_names = ['conv1_1','conv1_2','conv2_1','conv2_2',
                'up1_1', 'up1_2', 'up2_1', 'up2_2', 'conv_fin']
    
    print('copying layer weights')
    for name in layer_names:
        print(name)
        model_fcn01.get_layer(name).set_weights(model_fcn02.get_layer(name).get_weights())
        model_fcn01.get_layer(name).trainable = True
    
==> done
copying layer weights
conv1_1
conv1_2
conv2_1
conv2_2
up1_1
up1_2
up2_1
up2_2
conv_fin
In [9]:
    # 損失関数,最適化手法を定義
    adam = Adam(lr=1e-5)
    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.1, nesterov=True)
    #rmsprop = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
    model_fcn01.compile(optimizer=adam, loss=dice_coef_loss, metrics=[dice_coef])

    # 構造・重みを保存するディレクトリーの有無を確認
    dpath_checkpoints = os.path.join(dpath_this, dname_checkpoints)
    if not os.path.isdir(dpath_checkpoints):
        os.mkdir(dpath_checkpoints)

    # 重みを保存するためのオブジェクトを用意
    fname_weights = "model_weights_{epoch:02d}.h5"
    fpath_weights = os.path.join(dpath_checkpoints, fname_weights)
    checkpointer = ModelCheckpoint(filepath=fpath_weights, save_best_only=False)      
In [10]:
    # トレーニングを開始
    print('start training...')
    history = model_fcn01.fit(X_train, Y_train, batch_size=64, epochs=400, verbose=1,
                  shuffle=True, validation_data=(X_valid, Y_valid), callbacks=[checkpointer])
start training...
Train on 1452 samples, validate on 527 samples
Epoch 1/400
1452/1452 [==============================] - 83s 57ms/step - loss: -0.0796 - dice_coef: 0.0796 - val_loss: -0.1593 - val_dice_coef: 0.1593
Epoch 2/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.3290 - dice_coef: 0.3290 - val_loss: -0.5556 - val_dice_coef: 0.5556
Epoch 3/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.6375 - dice_coef: 0.6375 - val_loss: -0.6944 - val_dice_coef: 0.6944
Epoch 4/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.7325 - dice_coef: 0.7325 - val_loss: -0.7432 - val_dice_coef: 0.7432
Epoch 5/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.7637 - dice_coef: 0.7637 - val_loss: -0.7764 - val_dice_coef: 0.7764
Epoch 6/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.7786 - dice_coef: 0.7786 - val_loss: -0.7709 - val_dice_coef: 0.7709
Epoch 7/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.7870 - dice_coef: 0.7870 - val_loss: -0.7904 - val_dice_coef: 0.7904
Epoch 8/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8000 - dice_coef: 0.8000 - val_loss: -0.8064 - val_dice_coef: 0.8064
Epoch 9/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8064 - dice_coef: 0.8064 - val_loss: -0.8101 - val_dice_coef: 0.8101
Epoch 10/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8106 - dice_coef: 0.8106 - val_loss: -0.8141 - val_dice_coef: 0.8141
Epoch 11/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8102 - dice_coef: 0.8102 - val_loss: -0.8004 - val_dice_coef: 0.8004
Epoch 12/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8129 - dice_coef: 0.8129 - val_loss: -0.8218 - val_dice_coef: 0.8218
Epoch 13/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8128 - dice_coef: 0.8128 - val_loss: -0.8217 - val_dice_coef: 0.8217
Epoch 14/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8226 - dice_coef: 0.8226 - val_loss: -0.8168 - val_dice_coef: 0.8168
Epoch 15/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8280 - dice_coef: 0.8280 - val_loss: -0.8293 - val_dice_coef: 0.8293
Epoch 16/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8228 - dice_coef: 0.8228 - val_loss: -0.8201 - val_dice_coef: 0.8201
Epoch 17/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8326 - dice_coef: 0.8326 - val_loss: -0.8174 - val_dice_coef: 0.8174
Epoch 18/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8334 - dice_coef: 0.8334 - val_loss: -0.8224 - val_dice_coef: 0.8224
Epoch 19/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8350 - dice_coef: 0.8350 - val_loss: -0.8316 - val_dice_coef: 0.8316
Epoch 20/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8389 - dice_coef: 0.8389 - val_loss: -0.8386 - val_dice_coef: 0.8386
Epoch 21/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8317 - dice_coef: 0.8317 - val_loss: -0.8389 - val_dice_coef: 0.8389
Epoch 22/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8400 - dice_coef: 0.8400 - val_loss: -0.8386 - val_dice_coef: 0.8386
Epoch 23/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8370 - dice_coef: 0.8370 - val_loss: -0.8363 - val_dice_coef: 0.8363
Epoch 24/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8396 - dice_coef: 0.8396 - val_loss: -0.8358 - val_dice_coef: 0.8358
Epoch 25/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8442 - dice_coef: 0.8442 - val_loss: -0.8287 - val_dice_coef: 0.8287
Epoch 26/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8424 - dice_coef: 0.8424 - val_loss: -0.8403 - val_dice_coef: 0.8403
Epoch 27/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8247 - dice_coef: 0.8247 - val_loss: -0.8007 - val_dice_coef: 0.8007
Epoch 28/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8408 - dice_coef: 0.8408 - val_loss: -0.8351 - val_dice_coef: 0.8351
Epoch 29/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8473 - dice_coef: 0.8473 - val_loss: -0.8449 - val_dice_coef: 0.8449
Epoch 30/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8414 - dice_coef: 0.8414 - val_loss: -0.8250 - val_dice_coef: 0.8250
Epoch 31/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8346 - dice_coef: 0.8346 - val_loss: -0.8387 - val_dice_coef: 0.8387
Epoch 32/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8504 - dice_coef: 0.8504 - val_loss: -0.8384 - val_dice_coef: 0.8384
Epoch 33/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8534 - dice_coef: 0.8534 - val_loss: -0.8476 - val_dice_coef: 0.8476
Epoch 34/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8533 - dice_coef: 0.8533 - val_loss: -0.8371 - val_dice_coef: 0.8371
Epoch 35/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8552 - dice_coef: 0.8552 - val_loss: -0.8451 - val_dice_coef: 0.8451
Epoch 36/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8572 - dice_coef: 0.8572 - val_loss: -0.8484 - val_dice_coef: 0.8484
Epoch 37/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8562 - dice_coef: 0.8562 - val_loss: -0.8422 - val_dice_coef: 0.8422
Epoch 38/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8587 - dice_coef: 0.8587 - val_loss: -0.8466 - val_dice_coef: 0.8466
Epoch 39/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8572 - dice_coef: 0.8572 - val_loss: -0.8407 - val_dice_coef: 0.8407
Epoch 40/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8600 - dice_coef: 0.8600 - val_loss: -0.8473 - val_dice_coef: 0.8473
Epoch 41/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8612 - dice_coef: 0.8612 - val_loss: -0.8491 - val_dice_coef: 0.8491
Epoch 42/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8625 - dice_coef: 0.8625 - val_loss: -0.8467 - val_dice_coef: 0.8467
Epoch 43/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8559 - dice_coef: 0.8559 - val_loss: -0.8498 - val_dice_coef: 0.8498
Epoch 44/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8568 - dice_coef: 0.8568 - val_loss: -0.8458 - val_dice_coef: 0.8458
Epoch 45/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8629 - dice_coef: 0.8629 - val_loss: -0.8271 - val_dice_coef: 0.8271
Epoch 46/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8611 - dice_coef: 0.8611 - val_loss: -0.8220 - val_dice_coef: 0.8220
Epoch 47/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8606 - dice_coef: 0.8606 - val_loss: -0.8504 - val_dice_coef: 0.8504
Epoch 48/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8668 - dice_coef: 0.8668 - val_loss: -0.8492 - val_dice_coef: 0.8492
Epoch 49/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8655 - dice_coef: 0.8655 - val_loss: -0.8546 - val_dice_coef: 0.8546
Epoch 50/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8690 - dice_coef: 0.8690 - val_loss: -0.8256 - val_dice_coef: 0.8256
Epoch 51/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8597 - dice_coef: 0.8597 - val_loss: -0.8319 - val_dice_coef: 0.8319
Epoch 52/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8665 - dice_coef: 0.8665 - val_loss: -0.8525 - val_dice_coef: 0.8525
Epoch 53/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8705 - dice_coef: 0.8705 - val_loss: -0.8556 - val_dice_coef: 0.8556
Epoch 54/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8696 - dice_coef: 0.8696 - val_loss: -0.8469 - val_dice_coef: 0.8469
Epoch 55/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8728 - dice_coef: 0.8728 - val_loss: -0.8545 - val_dice_coef: 0.8545
Epoch 56/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8678 - dice_coef: 0.8678 - val_loss: -0.8548 - val_dice_coef: 0.8548
Epoch 57/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8723 - dice_coef: 0.8723 - val_loss: -0.8557 - val_dice_coef: 0.8557
Epoch 58/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8715 - dice_coef: 0.8715 - val_loss: -0.8313 - val_dice_coef: 0.8313
Epoch 59/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8743 - dice_coef: 0.8743 - val_loss: -0.8535 - val_dice_coef: 0.8535
Epoch 60/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8753 - dice_coef: 0.8753 - val_loss: -0.8547 - val_dice_coef: 0.8547
Epoch 61/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8759 - dice_coef: 0.8759 - val_loss: -0.8584 - val_dice_coef: 0.8584
Epoch 62/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8727 - dice_coef: 0.8727 - val_loss: -0.8562 - val_dice_coef: 0.8562
Epoch 63/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8774 - dice_coef: 0.8774 - val_loss: -0.8545 - val_dice_coef: 0.8545
Epoch 64/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8796 - dice_coef: 0.8796 - val_loss: -0.8552 - val_dice_coef: 0.8552
Epoch 65/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8745 - dice_coef: 0.8745 - val_loss: -0.8483 - val_dice_coef: 0.8483
Epoch 66/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8802 - dice_coef: 0.8802 - val_loss: -0.8538 - val_dice_coef: 0.8538
Epoch 67/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8815 - dice_coef: 0.8815 - val_loss: -0.8563 - val_dice_coef: 0.8563
Epoch 68/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8768 - dice_coef: 0.8768 - val_loss: -0.8515 - val_dice_coef: 0.8515
Epoch 69/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8672 - dice_coef: 0.8672 - val_loss: -0.8550 - val_dice_coef: 0.8550
Epoch 70/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8768 - dice_coef: 0.8768 - val_loss: -0.8536 - val_dice_coef: 0.8536
Epoch 71/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8814 - dice_coef: 0.8814 - val_loss: -0.8355 - val_dice_coef: 0.8355
Epoch 72/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8750 - dice_coef: 0.8750 - val_loss: -0.8571 - val_dice_coef: 0.8571
Epoch 73/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8774 - dice_coef: 0.8774 - val_loss: -0.8566 - val_dice_coef: 0.8566
Epoch 74/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8763 - dice_coef: 0.8763 - val_loss: -0.8564 - val_dice_coef: 0.8564
Epoch 75/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8832 - dice_coef: 0.8832 - val_loss: -0.8281 - val_dice_coef: 0.8281
Epoch 76/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8791 - dice_coef: 0.8791 - val_loss: -0.8495 - val_dice_coef: 0.8495
Epoch 77/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8825 - dice_coef: 0.8825 - val_loss: -0.8530 - val_dice_coef: 0.8530
Epoch 78/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8860 - dice_coef: 0.8860 - val_loss: -0.8595 - val_dice_coef: 0.8595
Epoch 79/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8880 - dice_coef: 0.8880 - val_loss: -0.8582 - val_dice_coef: 0.8582
Epoch 80/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8894 - dice_coef: 0.8894 - val_loss: -0.8553 - val_dice_coef: 0.8553
Epoch 81/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8878 - dice_coef: 0.8878 - val_loss: -0.8619 - val_dice_coef: 0.8619
Epoch 82/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8852 - dice_coef: 0.8852 - val_loss: -0.8570 - val_dice_coef: 0.8570
Epoch 83/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8806 - dice_coef: 0.8806 - val_loss: -0.8634 - val_dice_coef: 0.8634
Epoch 84/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8912 - dice_coef: 0.8912 - val_loss: -0.8594 - val_dice_coef: 0.8594
Epoch 85/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8887 - dice_coef: 0.8887 - val_loss: -0.8548 - val_dice_coef: 0.8548
Epoch 86/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8929 - dice_coef: 0.8929 - val_loss: -0.8626 - val_dice_coef: 0.8626
Epoch 87/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8950 - dice_coef: 0.8950 - val_loss: -0.8536 - val_dice_coef: 0.8536
Epoch 88/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8954 - dice_coef: 0.8954 - val_loss: -0.8616 - val_dice_coef: 0.8616
Epoch 89/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8946 - dice_coef: 0.8946 - val_loss: -0.8626 - val_dice_coef: 0.8626
Epoch 90/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8904 - dice_coef: 0.8904 - val_loss: -0.8579 - val_dice_coef: 0.8579
Epoch 91/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8971 - dice_coef: 0.8971 - val_loss: -0.8430 - val_dice_coef: 0.8430
Epoch 92/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8963 - dice_coef: 0.8963 - val_loss: -0.8615 - val_dice_coef: 0.8615
Epoch 93/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8946 - dice_coef: 0.8946 - val_loss: -0.8629 - val_dice_coef: 0.8629
Epoch 94/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8981 - dice_coef: 0.8981 - val_loss: -0.8586 - val_dice_coef: 0.8586
Epoch 95/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8882 - dice_coef: 0.8882 - val_loss: -0.8540 - val_dice_coef: 0.8540
Epoch 96/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.8843 - dice_coef: 0.8843 - val_loss: -0.8156 - val_dice_coef: 0.8156
Epoch 97/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8875 - dice_coef: 0.8875 - val_loss: -0.8605 - val_dice_coef: 0.8605
Epoch 98/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9001 - dice_coef: 0.9001 - val_loss: -0.8586 - val_dice_coef: 0.8586
Epoch 99/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8985 - dice_coef: 0.8985 - val_loss: -0.8636 - val_dice_coef: 0.8636
Epoch 100/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9016 - dice_coef: 0.9016 - val_loss: -0.8564 - val_dice_coef: 0.8564
Epoch 101/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8997 - dice_coef: 0.8997 - val_loss: -0.8457 - val_dice_coef: 0.8457
Epoch 102/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.8925 - dice_coef: 0.8925 - val_loss: -0.8587 - val_dice_coef: 0.8587
Epoch 103/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9014 - dice_coef: 0.9014 - val_loss: -0.8596 - val_dice_coef: 0.8596
Epoch 104/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9072 - dice_coef: 0.9072 - val_loss: -0.8605 - val_dice_coef: 0.8605
Epoch 105/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9070 - dice_coef: 0.9070 - val_loss: -0.8615 - val_dice_coef: 0.8615
Epoch 106/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9097 - dice_coef: 0.9097 - val_loss: -0.8573 - val_dice_coef: 0.8573
Epoch 107/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9024 - dice_coef: 0.9024 - val_loss: -0.8612 - val_dice_coef: 0.8612
Epoch 108/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9034 - dice_coef: 0.9034 - val_loss: -0.8582 - val_dice_coef: 0.8582
Epoch 109/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9076 - dice_coef: 0.9076 - val_loss: -0.8470 - val_dice_coef: 0.8470
Epoch 110/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9098 - dice_coef: 0.9098 - val_loss: -0.8614 - val_dice_coef: 0.8614
Epoch 111/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9020 - dice_coef: 0.9020 - val_loss: -0.8526 - val_dice_coef: 0.8526
Epoch 112/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9011 - dice_coef: 0.9011 - val_loss: -0.8496 - val_dice_coef: 0.8496
Epoch 113/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9072 - dice_coef: 0.9072 - val_loss: -0.8408 - val_dice_coef: 0.8408
Epoch 114/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9036 - dice_coef: 0.9036 - val_loss: -0.8484 - val_dice_coef: 0.8484
Epoch 115/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9078 - dice_coef: 0.9078 - val_loss: -0.8620 - val_dice_coef: 0.8620
Epoch 116/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9132 - dice_coef: 0.9132 - val_loss: -0.8634 - val_dice_coef: 0.8634
Epoch 117/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9132 - dice_coef: 0.9132 - val_loss: -0.8635 - val_dice_coef: 0.8635
Epoch 118/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9048 - dice_coef: 0.9048 - val_loss: -0.8629 - val_dice_coef: 0.8629
Epoch 119/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9115 - dice_coef: 0.9115 - val_loss: -0.8596 - val_dice_coef: 0.8596
Epoch 120/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9135 - dice_coef: 0.9135 - val_loss: -0.8648 - val_dice_coef: 0.8648
Epoch 121/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9072 - dice_coef: 0.9072 - val_loss: -0.8640 - val_dice_coef: 0.8640
Epoch 122/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9090 - dice_coef: 0.9090 - val_loss: -0.8624 - val_dice_coef: 0.8624
Epoch 123/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9172 - dice_coef: 0.9172 - val_loss: -0.8665 - val_dice_coef: 0.8665
Epoch 124/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9135 - dice_coef: 0.9135 - val_loss: -0.8478 - val_dice_coef: 0.8478
Epoch 125/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9161 - dice_coef: 0.9161 - val_loss: -0.8631 - val_dice_coef: 0.8631
Epoch 126/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9194 - dice_coef: 0.9194 - val_loss: -0.8564 - val_dice_coef: 0.8564
Epoch 127/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9206 - dice_coef: 0.9206 - val_loss: -0.8541 - val_dice_coef: 0.8541
Epoch 128/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9214 - dice_coef: 0.9214 - val_loss: -0.8542 - val_dice_coef: 0.8542
Epoch 129/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9146 - dice_coef: 0.9146 - val_loss: -0.8642 - val_dice_coef: 0.8642
Epoch 130/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9186 - dice_coef: 0.9186 - val_loss: -0.8605 - val_dice_coef: 0.8605
Epoch 131/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9207 - dice_coef: 0.9207 - val_loss: -0.8610 - val_dice_coef: 0.8610
Epoch 132/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9188 - dice_coef: 0.9188 - val_loss: -0.8537 - val_dice_coef: 0.8537
Epoch 133/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.8957 - dice_coef: 0.8957 - val_loss: -0.8626 - val_dice_coef: 0.8626
Epoch 134/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9184 - dice_coef: 0.9184 - val_loss: -0.8645 - val_dice_coef: 0.8645
Epoch 135/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9226 - dice_coef: 0.9226 - val_loss: -0.8622 - val_dice_coef: 0.8622
Epoch 136/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9244 - dice_coef: 0.9244 - val_loss: -0.8554 - val_dice_coef: 0.8554
Epoch 137/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9239 - dice_coef: 0.9239 - val_loss: -0.8535 - val_dice_coef: 0.8535
Epoch 138/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9251 - dice_coef: 0.9251 - val_loss: -0.8604 - val_dice_coef: 0.8604
Epoch 139/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9262 - dice_coef: 0.9262 - val_loss: -0.8603 - val_dice_coef: 0.8603
Epoch 140/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9217 - dice_coef: 0.9217 - val_loss: -0.8604 - val_dice_coef: 0.8604
Epoch 141/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9257 - dice_coef: 0.9257 - val_loss: -0.8648 - val_dice_coef: 0.8648
Epoch 142/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9294 - dice_coef: 0.9294 - val_loss: -0.8543 - val_dice_coef: 0.8543
Epoch 143/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9283 - dice_coef: 0.9283 - val_loss: -0.8621 - val_dice_coef: 0.8621
Epoch 144/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9239 - dice_coef: 0.9239 - val_loss: -0.8632 - val_dice_coef: 0.8632
Epoch 145/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9176 - dice_coef: 0.9176 - val_loss: -0.8537 - val_dice_coef: 0.8537
Epoch 146/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9285 - dice_coef: 0.9285 - val_loss: -0.8547 - val_dice_coef: 0.8547
Epoch 147/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9252 - dice_coef: 0.9252 - val_loss: -0.8395 - val_dice_coef: 0.8395
Epoch 148/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9175 - dice_coef: 0.9175 - val_loss: -0.8532 - val_dice_coef: 0.8532
Epoch 149/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9270 - dice_coef: 0.9270 - val_loss: -0.8619 - val_dice_coef: 0.8619
Epoch 150/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9311 - dice_coef: 0.9311 - val_loss: -0.8449 - val_dice_coef: 0.8449
Epoch 151/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9314 - dice_coef: 0.9314 - val_loss: -0.8516 - val_dice_coef: 0.8516
Epoch 152/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9298 - dice_coef: 0.9298 - val_loss: -0.8607 - val_dice_coef: 0.8607
Epoch 153/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9303 - dice_coef: 0.9303 - val_loss: -0.8583 - val_dice_coef: 0.8583
Epoch 154/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9359 - dice_coef: 0.9359 - val_loss: -0.8585 - val_dice_coef: 0.8585
Epoch 155/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9255 - dice_coef: 0.9255 - val_loss: -0.8383 - val_dice_coef: 0.8383
Epoch 156/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9303 - dice_coef: 0.9303 - val_loss: -0.8548 - val_dice_coef: 0.8548
Epoch 157/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9325 - dice_coef: 0.9325 - val_loss: -0.8615 - val_dice_coef: 0.8615
Epoch 158/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9362 - dice_coef: 0.9362 - val_loss: -0.8583 - val_dice_coef: 0.8583
Epoch 159/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9371 - dice_coef: 0.9371 - val_loss: -0.8618 - val_dice_coef: 0.8618
Epoch 160/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9347 - dice_coef: 0.9347 - val_loss: -0.8638 - val_dice_coef: 0.8638
Epoch 161/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9361 - dice_coef: 0.9361 - val_loss: -0.8529 - val_dice_coef: 0.8529
Epoch 162/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9368 - dice_coef: 0.9368 - val_loss: -0.8594 - val_dice_coef: 0.8594
Epoch 163/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9389 - dice_coef: 0.9389 - val_loss: -0.8431 - val_dice_coef: 0.8431
Epoch 164/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9379 - dice_coef: 0.9379 - val_loss: -0.8464 - val_dice_coef: 0.8464
Epoch 165/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9374 - dice_coef: 0.9374 - val_loss: -0.8606 - val_dice_coef: 0.8606
Epoch 166/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9402 - dice_coef: 0.9402 - val_loss: -0.8552 - val_dice_coef: 0.8552
Epoch 167/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9307 - dice_coef: 0.9307 - val_loss: -0.8536 - val_dice_coef: 0.8536
Epoch 168/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9330 - dice_coef: 0.9330 - val_loss: -0.8603 - val_dice_coef: 0.8603
Epoch 169/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9422 - dice_coef: 0.9422 - val_loss: -0.8558 - val_dice_coef: 0.8558
Epoch 170/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9441 - dice_coef: 0.9441 - val_loss: -0.8584 - val_dice_coef: 0.8584
Epoch 171/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9323 - dice_coef: 0.9323 - val_loss: -0.8322 - val_dice_coef: 0.8322
Epoch 172/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9270 - dice_coef: 0.9270 - val_loss: -0.8539 - val_dice_coef: 0.8539
Epoch 173/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9377 - dice_coef: 0.9377 - val_loss: -0.8620 - val_dice_coef: 0.8620
Epoch 174/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9426 - dice_coef: 0.9426 - val_loss: -0.8546 - val_dice_coef: 0.8546
Epoch 175/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9359 - dice_coef: 0.9359 - val_loss: -0.8608 - val_dice_coef: 0.8608
Epoch 176/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9317 - dice_coef: 0.9317 - val_loss: -0.8524 - val_dice_coef: 0.8524
Epoch 177/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9317 - dice_coef: 0.9317 - val_loss: -0.8626 - val_dice_coef: 0.8626
Epoch 178/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9300 - dice_coef: 0.9300 - val_loss: -0.8474 - val_dice_coef: 0.8474
Epoch 179/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9336 - dice_coef: 0.9336 - val_loss: -0.8514 - val_dice_coef: 0.8514
Epoch 180/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9441 - dice_coef: 0.9441 - val_loss: -0.8638 - val_dice_coef: 0.8638
Epoch 181/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9447 - dice_coef: 0.9447 - val_loss: -0.8602 - val_dice_coef: 0.8602
Epoch 182/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9474 - dice_coef: 0.9474 - val_loss: -0.8617 - val_dice_coef: 0.8617
Epoch 183/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9474 - dice_coef: 0.9474 - val_loss: -0.8484 - val_dice_coef: 0.8484
Epoch 184/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9463 - dice_coef: 0.9463 - val_loss: -0.8538 - val_dice_coef: 0.8538
Epoch 185/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9449 - dice_coef: 0.9449 - val_loss: -0.8529 - val_dice_coef: 0.8529
Epoch 186/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9481 - dice_coef: 0.9481 - val_loss: -0.8586 - val_dice_coef: 0.8586
Epoch 187/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9426 - dice_coef: 0.9426 - val_loss: -0.8619 - val_dice_coef: 0.8619
Epoch 188/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9432 - dice_coef: 0.9432 - val_loss: -0.8454 - val_dice_coef: 0.8454
Epoch 189/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9429 - dice_coef: 0.9429 - val_loss: -0.8578 - val_dice_coef: 0.8578
Epoch 190/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9469 - dice_coef: 0.9469 - val_loss: -0.8577 - val_dice_coef: 0.8577
Epoch 191/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9474 - dice_coef: 0.9474 - val_loss: -0.8528 - val_dice_coef: 0.8528
Epoch 192/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9476 - dice_coef: 0.9476 - val_loss: -0.8491 - val_dice_coef: 0.8491
Epoch 193/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9432 - dice_coef: 0.9432 - val_loss: -0.8610 - val_dice_coef: 0.8610
Epoch 194/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9476 - dice_coef: 0.9476 - val_loss: -0.8575 - val_dice_coef: 0.8575
Epoch 195/400
1452/1452 [==============================] - 56s 39ms/step - loss: -0.9485 - dice_coef: 0.9485 - val_loss: -0.8619 - val_dice_coef: 0.8619
Epoch 196/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9455 - dice_coef: 0.9455 - val_loss: -0.8600 - val_dice_coef: 0.8600
Epoch 197/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9500 - dice_coef: 0.9500 - val_loss: -0.8560 - val_dice_coef: 0.8560
Epoch 198/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9537 - dice_coef: 0.9537 - val_loss: -0.8409 - val_dice_coef: 0.8409
Epoch 199/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9501 - dice_coef: 0.9501 - val_loss: -0.8612 - val_dice_coef: 0.8612
Epoch 200/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9487 - dice_coef: 0.9487 - val_loss: -0.8588 - val_dice_coef: 0.8588
Epoch 201/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9480 - dice_coef: 0.9480 - val_loss: -0.8555 - val_dice_coef: 0.8555
Epoch 202/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9424 - dice_coef: 0.9424 - val_loss: -0.8628 - val_dice_coef: 0.8628
Epoch 203/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9438 - dice_coef: 0.9438 - val_loss: -0.8531 - val_dice_coef: 0.8531
Epoch 204/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9513 - dice_coef: 0.9513 - val_loss: -0.8514 - val_dice_coef: 0.8514
Epoch 205/400
1452/1452 [==============================] - 56s 38ms/step - loss: -0.9509 - dice_coef: 0.9509 - val_loss: -0.8503 - val_dice_coef: 0.8503
Epoch 206/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9483 - dice_coef: 0.9483 - val_loss: -0.8566 - val_dice_coef: 0.8566
Epoch 207/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9455 - dice_coef: 0.9455 - val_loss: -0.8502 - val_dice_coef: 0.8502
Epoch 208/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9341 - dice_coef: 0.9341 - val_loss: -0.8661 - val_dice_coef: 0.8661
Epoch 209/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9440 - dice_coef: 0.9440 - val_loss: -0.8601 - val_dice_coef: 0.8601
Epoch 210/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9534 - dice_coef: 0.9534 - val_loss: -0.8629 - val_dice_coef: 0.8629
Epoch 211/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9525 - dice_coef: 0.9525 - val_loss: -0.8617 - val_dice_coef: 0.8617
Epoch 212/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9551 - dice_coef: 0.9551 - val_loss: -0.8524 - val_dice_coef: 0.8524
Epoch 213/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9558 - dice_coef: 0.9558 - val_loss: -0.8617 - val_dice_coef: 0.8617
Epoch 214/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9538 - dice_coef: 0.9538 - val_loss: -0.8570 - val_dice_coef: 0.8570
Epoch 215/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9557 - dice_coef: 0.9557 - val_loss: -0.8588 - val_dice_coef: 0.8588
Epoch 216/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9570 - dice_coef: 0.9570 - val_loss: -0.8618 - val_dice_coef: 0.8618
Epoch 217/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9545 - dice_coef: 0.9545 - val_loss: -0.8513 - val_dice_coef: 0.8513
Epoch 218/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9569 - dice_coef: 0.9569 - val_loss: -0.8537 - val_dice_coef: 0.8537
Epoch 219/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9555 - dice_coef: 0.9555 - val_loss: -0.8630 - val_dice_coef: 0.8630
Epoch 220/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9529 - dice_coef: 0.9529 - val_loss: -0.8551 - val_dice_coef: 0.8551
Epoch 221/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9557 - dice_coef: 0.9557 - val_loss: -0.8599 - val_dice_coef: 0.8599
Epoch 222/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9557 - dice_coef: 0.9557 - val_loss: -0.8634 - val_dice_coef: 0.8634
Epoch 223/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9586 - dice_coef: 0.9586 - val_loss: -0.8543 - val_dice_coef: 0.8543
Epoch 224/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9573 - dice_coef: 0.9573 - val_loss: -0.8619 - val_dice_coef: 0.8619
Epoch 225/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9311 - dice_coef: 0.9311 - val_loss: -0.8647 - val_dice_coef: 0.8647
Epoch 226/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9510 - dice_coef: 0.9510 - val_loss: -0.8538 - val_dice_coef: 0.8538
Epoch 227/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9565 - dice_coef: 0.9565 - val_loss: -0.8515 - val_dice_coef: 0.8515
Epoch 228/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9603 - dice_coef: 0.9603 - val_loss: -0.8455 - val_dice_coef: 0.8455
Epoch 229/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9590 - dice_coef: 0.9590 - val_loss: -0.8603 - val_dice_coef: 0.8603
Epoch 230/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9578 - dice_coef: 0.9578 - val_loss: -0.8553 - val_dice_coef: 0.8553
Epoch 231/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9592 - dice_coef: 0.9592 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 232/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9591 - dice_coef: 0.9591 - val_loss: -0.8499 - val_dice_coef: 0.8499
Epoch 233/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9569 - dice_coef: 0.9569 - val_loss: -0.8611 - val_dice_coef: 0.8611
Epoch 234/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9606 - dice_coef: 0.9606 - val_loss: -0.8580 - val_dice_coef: 0.8580
Epoch 235/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9616 - dice_coef: 0.9616 - val_loss: -0.8527 - val_dice_coef: 0.8527
Epoch 236/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9578 - dice_coef: 0.9578 - val_loss: -0.8526 - val_dice_coef: 0.8526
Epoch 237/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9551 - dice_coef: 0.9551 - val_loss: -0.8505 - val_dice_coef: 0.8505
Epoch 238/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9551 - dice_coef: 0.9551 - val_loss: -0.8549 - val_dice_coef: 0.8549
Epoch 239/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9590 - dice_coef: 0.9590 - val_loss: -0.8598 - val_dice_coef: 0.8598
Epoch 240/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9589 - dice_coef: 0.9589 - val_loss: -0.8614 - val_dice_coef: 0.8614
Epoch 241/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9583 - dice_coef: 0.9583 - val_loss: -0.8561 - val_dice_coef: 0.8561
Epoch 242/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9528 - dice_coef: 0.9528 - val_loss: -0.8579 - val_dice_coef: 0.8579
Epoch 243/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9550 - dice_coef: 0.9550 - val_loss: -0.8586 - val_dice_coef: 0.8586
Epoch 244/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9576 - dice_coef: 0.9576 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 245/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9605 - dice_coef: 0.9605 - val_loss: -0.8474 - val_dice_coef: 0.8474
Epoch 246/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9608 - dice_coef: 0.9608 - val_loss: -0.8563 - val_dice_coef: 0.8563
Epoch 247/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9638 - dice_coef: 0.9638 - val_loss: -0.8492 - val_dice_coef: 0.8492
Epoch 248/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9596 - dice_coef: 0.9596 - val_loss: -0.8564 - val_dice_coef: 0.8564
Epoch 249/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9603 - dice_coef: 0.9603 - val_loss: -0.8601 - val_dice_coef: 0.8601
Epoch 250/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9570 - dice_coef: 0.9570 - val_loss: -0.8519 - val_dice_coef: 0.8519
Epoch 251/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9613 - dice_coef: 0.9613 - val_loss: -0.8498 - val_dice_coef: 0.8498
Epoch 252/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9618 - dice_coef: 0.9618 - val_loss: -0.8596 - val_dice_coef: 0.8596
Epoch 253/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9637 - dice_coef: 0.9637 - val_loss: -0.8594 - val_dice_coef: 0.8594
Epoch 254/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9614 - dice_coef: 0.9614 - val_loss: -0.8481 - val_dice_coef: 0.8481
Epoch 255/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9614 - dice_coef: 0.9614 - val_loss: -0.8521 - val_dice_coef: 0.8521
Epoch 256/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9487 - dice_coef: 0.9487 - val_loss: -0.8401 - val_dice_coef: 0.8401
Epoch 257/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9576 - dice_coef: 0.9576 - val_loss: -0.8556 - val_dice_coef: 0.8556
Epoch 258/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9612 - dice_coef: 0.9612 - val_loss: -0.8557 - val_dice_coef: 0.8557
Epoch 259/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9627 - dice_coef: 0.9627 - val_loss: -0.8489 - val_dice_coef: 0.8489
Epoch 260/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9621 - dice_coef: 0.9621 - val_loss: -0.8595 - val_dice_coef: 0.8595
Epoch 261/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9642 - dice_coef: 0.9642 - val_loss: -0.8586 - val_dice_coef: 0.8586
Epoch 262/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9600 - dice_coef: 0.9600 - val_loss: -0.8508 - val_dice_coef: 0.8508
Epoch 263/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9641 - dice_coef: 0.9641 - val_loss: -0.8378 - val_dice_coef: 0.8378
Epoch 264/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9636 - dice_coef: 0.9636 - val_loss: -0.8583 - val_dice_coef: 0.8583
Epoch 265/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9645 - dice_coef: 0.9645 - val_loss: -0.8574 - val_dice_coef: 0.8574
Epoch 266/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9631 - dice_coef: 0.9631 - val_loss: -0.8482 - val_dice_coef: 0.8482
Epoch 267/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9664 - dice_coef: 0.9664 - val_loss: -0.8523 - val_dice_coef: 0.8523
Epoch 268/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9665 - dice_coef: 0.9665 - val_loss: -0.8471 - val_dice_coef: 0.8471
Epoch 269/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9639 - dice_coef: 0.9639 - val_loss: -0.8596 - val_dice_coef: 0.8596
Epoch 270/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9633 - dice_coef: 0.9633 - val_loss: -0.8589 - val_dice_coef: 0.8589
Epoch 271/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9663 - dice_coef: 0.9663 - val_loss: -0.8413 - val_dice_coef: 0.8413
Epoch 272/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9524 - dice_coef: 0.9524 - val_loss: -0.8561 - val_dice_coef: 0.8561
Epoch 273/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9624 - dice_coef: 0.9624 - val_loss: -0.8509 - val_dice_coef: 0.8509
Epoch 274/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9658 - dice_coef: 0.9658 - val_loss: -0.8554 - val_dice_coef: 0.8554
Epoch 275/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9668 - dice_coef: 0.9668 - val_loss: -0.8605 - val_dice_coef: 0.8605
Epoch 276/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9681 - dice_coef: 0.9681 - val_loss: -0.8558 - val_dice_coef: 0.8558
Epoch 277/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9648 - dice_coef: 0.9648 - val_loss: -0.8588 - val_dice_coef: 0.8588
Epoch 278/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9626 - dice_coef: 0.9626 - val_loss: -0.8572 - val_dice_coef: 0.8572
Epoch 279/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9594 - dice_coef: 0.9594 - val_loss: -0.8589 - val_dice_coef: 0.8589
Epoch 280/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9590 - dice_coef: 0.9590 - val_loss: -0.8463 - val_dice_coef: 0.8463
Epoch 281/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9661 - dice_coef: 0.9661 - val_loss: -0.8599 - val_dice_coef: 0.8599
Epoch 282/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9648 - dice_coef: 0.9648 - val_loss: -0.8570 - val_dice_coef: 0.8570
Epoch 283/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9686 - dice_coef: 0.9686 - val_loss: -0.8590 - val_dice_coef: 0.8590
Epoch 284/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9649 - dice_coef: 0.9649 - val_loss: -0.8507 - val_dice_coef: 0.8507
Epoch 285/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9684 - dice_coef: 0.9684 - val_loss: -0.8539 - val_dice_coef: 0.8539
Epoch 286/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9665 - dice_coef: 0.9665 - val_loss: -0.8553 - val_dice_coef: 0.8553
Epoch 287/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9659 - dice_coef: 0.9659 - val_loss: -0.8538 - val_dice_coef: 0.8538
Epoch 288/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9664 - dice_coef: 0.9664 - val_loss: -0.8550 - val_dice_coef: 0.8550
Epoch 289/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9577 - dice_coef: 0.9577 - val_loss: -0.8475 - val_dice_coef: 0.8475
Epoch 290/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9552 - dice_coef: 0.9552 - val_loss: -0.8515 - val_dice_coef: 0.8515
Epoch 291/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9640 - dice_coef: 0.9640 - val_loss: -0.8549 - val_dice_coef: 0.8549
Epoch 292/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9690 - dice_coef: 0.9690 - val_loss: -0.8478 - val_dice_coef: 0.8478
Epoch 293/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9699 - dice_coef: 0.9699 - val_loss: -0.8603 - val_dice_coef: 0.8603
Epoch 294/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9646 - dice_coef: 0.9646 - val_loss: -0.8516 - val_dice_coef: 0.8516
Epoch 295/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9664 - dice_coef: 0.9664 - val_loss: -0.8501 - val_dice_coef: 0.8501
Epoch 296/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9675 - dice_coef: 0.9675 - val_loss: -0.8561 - val_dice_coef: 0.8561
Epoch 297/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9682 - dice_coef: 0.9682 - val_loss: -0.8531 - val_dice_coef: 0.8531
Epoch 298/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9656 - dice_coef: 0.9656 - val_loss: -0.8570 - val_dice_coef: 0.8570
Epoch 299/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9588 - dice_coef: 0.9588 - val_loss: -0.8597 - val_dice_coef: 0.8597
Epoch 300/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9577 - dice_coef: 0.9577 - val_loss: -0.8620 - val_dice_coef: 0.8620
Epoch 301/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9582 - dice_coef: 0.9582 - val_loss: -0.8376 - val_dice_coef: 0.8376
Epoch 302/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9642 - dice_coef: 0.9642 - val_loss: -0.8589 - val_dice_coef: 0.8589
Epoch 303/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9675 - dice_coef: 0.9675 - val_loss: -0.8453 - val_dice_coef: 0.8453
Epoch 304/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9661 - dice_coef: 0.9661 - val_loss: -0.8590 - val_dice_coef: 0.8590
Epoch 305/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9642 - dice_coef: 0.9642 - val_loss: -0.8518 - val_dice_coef: 0.8518
Epoch 306/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9655 - dice_coef: 0.9655 - val_loss: -0.8580 - val_dice_coef: 0.8580
Epoch 307/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9665 - dice_coef: 0.9665 - val_loss: -0.8512 - val_dice_coef: 0.8512
Epoch 308/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9638 - dice_coef: 0.9638 - val_loss: -0.8369 - val_dice_coef: 0.8369
Epoch 309/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9613 - dice_coef: 0.9613 - val_loss: -0.8518 - val_dice_coef: 0.8518
Epoch 310/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9688 - dice_coef: 0.9688 - val_loss: -0.8567 - val_dice_coef: 0.8567
Epoch 311/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9699 - dice_coef: 0.9699 - val_loss: -0.8538 - val_dice_coef: 0.8538
Epoch 312/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9728 - dice_coef: 0.9728 - val_loss: -0.8577 - val_dice_coef: 0.8577
Epoch 313/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9691 - dice_coef: 0.9691 - val_loss: -0.8514 - val_dice_coef: 0.8514
Epoch 314/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9687 - dice_coef: 0.9687 - val_loss: -0.8499 - val_dice_coef: 0.8499
Epoch 315/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9722 - dice_coef: 0.9722 - val_loss: -0.8521 - val_dice_coef: 0.8521
Epoch 316/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9733 - dice_coef: 0.9733 - val_loss: -0.8549 - val_dice_coef: 0.8549
Epoch 317/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9738 - dice_coef: 0.9738 - val_loss: -0.8545 - val_dice_coef: 0.8545
Epoch 318/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9730 - dice_coef: 0.9730 - val_loss: -0.8555 - val_dice_coef: 0.8555
Epoch 319/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9719 - dice_coef: 0.9719 - val_loss: -0.8523 - val_dice_coef: 0.8523
Epoch 320/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9724 - dice_coef: 0.9724 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 321/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9718 - dice_coef: 0.9718 - val_loss: -0.8559 - val_dice_coef: 0.8559
Epoch 322/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9676 - dice_coef: 0.9676 - val_loss: -0.8462 - val_dice_coef: 0.8462
Epoch 323/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9665 - dice_coef: 0.9665 - val_loss: -0.8432 - val_dice_coef: 0.8432
Epoch 324/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9650 - dice_coef: 0.9650 - val_loss: -0.8589 - val_dice_coef: 0.8589
Epoch 325/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9580 - dice_coef: 0.9580 - val_loss: -0.8568 - val_dice_coef: 0.8568
Epoch 326/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9686 - dice_coef: 0.9686 - val_loss: -0.8498 - val_dice_coef: 0.8498
Epoch 327/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9703 - dice_coef: 0.9703 - val_loss: -0.8487 - val_dice_coef: 0.8487
Epoch 328/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9722 - dice_coef: 0.9722 - val_loss: -0.8547 - val_dice_coef: 0.8547
Epoch 329/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9737 - dice_coef: 0.9737 - val_loss: -0.8552 - val_dice_coef: 0.8552
Epoch 330/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9747 - dice_coef: 0.9747 - val_loss: -0.8539 - val_dice_coef: 0.8539
Epoch 331/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9743 - dice_coef: 0.9743 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 332/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9738 - dice_coef: 0.9738 - val_loss: -0.8564 - val_dice_coef: 0.8564
Epoch 333/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9722 - dice_coef: 0.9722 - val_loss: -0.8540 - val_dice_coef: 0.8540
Epoch 334/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9737 - dice_coef: 0.9737 - val_loss: -0.8561 - val_dice_coef: 0.8561
Epoch 335/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9733 - dice_coef: 0.9733 - val_loss: -0.8588 - val_dice_coef: 0.8588
Epoch 336/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9714 - dice_coef: 0.9714 - val_loss: -0.8561 - val_dice_coef: 0.8561
Epoch 337/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9738 - dice_coef: 0.9738 - val_loss: -0.8530 - val_dice_coef: 0.8530
Epoch 338/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9668 - dice_coef: 0.9668 - val_loss: -0.8458 - val_dice_coef: 0.8458
Epoch 339/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9697 - dice_coef: 0.9697 - val_loss: -0.8519 - val_dice_coef: 0.8519
Epoch 340/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9732 - dice_coef: 0.9732 - val_loss: -0.8488 - val_dice_coef: 0.8488
Epoch 341/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9725 - dice_coef: 0.9725 - val_loss: -0.8544 - val_dice_coef: 0.8544
Epoch 342/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9736 - dice_coef: 0.9736 - val_loss: -0.8580 - val_dice_coef: 0.8580
Epoch 343/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9747 - dice_coef: 0.9747 - val_loss: -0.8487 - val_dice_coef: 0.8487
Epoch 344/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9713 - dice_coef: 0.9713 - val_loss: -0.8448 - val_dice_coef: 0.8448
Epoch 345/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9693 - dice_coef: 0.9693 - val_loss: -0.8544 - val_dice_coef: 0.8544
Epoch 346/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9725 - dice_coef: 0.9725 - val_loss: -0.8540 - val_dice_coef: 0.8540
Epoch 347/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9717 - dice_coef: 0.9717 - val_loss: -0.8511 - val_dice_coef: 0.8511
Epoch 348/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9697 - dice_coef: 0.9697 - val_loss: -0.8573 - val_dice_coef: 0.8573
Epoch 349/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9679 - dice_coef: 0.9679 - val_loss: -0.8554 - val_dice_coef: 0.8554
Epoch 350/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9694 - dice_coef: 0.9694 - val_loss: -0.8540 - val_dice_coef: 0.8540
Epoch 351/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9690 - dice_coef: 0.9690 - val_loss: -0.8536 - val_dice_coef: 0.8536
Epoch 352/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9694 - dice_coef: 0.9694 - val_loss: -0.8512 - val_dice_coef: 0.8512
Epoch 353/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9698 - dice_coef: 0.9698 - val_loss: -0.8505 - val_dice_coef: 0.8505
Epoch 354/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9668 - dice_coef: 0.9668 - val_loss: -0.8594 - val_dice_coef: 0.8594
Epoch 355/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9461 - dice_coef: 0.9461 - val_loss: -0.8441 - val_dice_coef: 0.8441
Epoch 356/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9626 - dice_coef: 0.9626 - val_loss: -0.8512 - val_dice_coef: 0.8512
Epoch 357/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9627 - dice_coef: 0.9627 - val_loss: -0.8596 - val_dice_coef: 0.8596
Epoch 358/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9679 - dice_coef: 0.9679 - val_loss: -0.8578 - val_dice_coef: 0.8578
Epoch 359/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9631 - dice_coef: 0.9631 - val_loss: -0.8526 - val_dice_coef: 0.8526
Epoch 360/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9688 - dice_coef: 0.9688 - val_loss: -0.8554 - val_dice_coef: 0.8554
Epoch 361/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9748 - dice_coef: 0.9748 - val_loss: -0.8545 - val_dice_coef: 0.8545
Epoch 362/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9748 - dice_coef: 0.9748 - val_loss: -0.8494 - val_dice_coef: 0.8494
Epoch 363/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9763 - dice_coef: 0.9763 - val_loss: -0.8537 - val_dice_coef: 0.8537
Epoch 364/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9734 - dice_coef: 0.9734 - val_loss: -0.8612 - val_dice_coef: 0.8612
Epoch 365/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9632 - dice_coef: 0.9632 - val_loss: -0.8574 - val_dice_coef: 0.8574
Epoch 366/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9617 - dice_coef: 0.9617 - val_loss: -0.8504 - val_dice_coef: 0.8504
Epoch 367/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9722 - dice_coef: 0.9722 - val_loss: -0.8591 - val_dice_coef: 0.8591
Epoch 368/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9735 - dice_coef: 0.9735 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 369/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9755 - dice_coef: 0.9755 - val_loss: -0.8576 - val_dice_coef: 0.8576
Epoch 370/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9748 - dice_coef: 0.9748 - val_loss: -0.8431 - val_dice_coef: 0.8431
Epoch 371/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9726 - dice_coef: 0.9726 - val_loss: -0.8552 - val_dice_coef: 0.8552
Epoch 372/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9751 - dice_coef: 0.9751 - val_loss: -0.8559 - val_dice_coef: 0.8559
Epoch 373/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9743 - dice_coef: 0.9743 - val_loss: -0.8525 - val_dice_coef: 0.8525
Epoch 374/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9736 - dice_coef: 0.9736 - val_loss: -0.8574 - val_dice_coef: 0.8574
Epoch 375/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9726 - dice_coef: 0.9726 - val_loss: -0.8593 - val_dice_coef: 0.8593
Epoch 376/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9657 - dice_coef: 0.9657 - val_loss: -0.8562 - val_dice_coef: 0.8562
Epoch 377/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9681 - dice_coef: 0.9681 - val_loss: -0.8559 - val_dice_coef: 0.8559
Epoch 378/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9734 - dice_coef: 0.9734 - val_loss: -0.8538 - val_dice_coef: 0.8538
Epoch 379/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9752 - dice_coef: 0.9752 - val_loss: -0.8523 - val_dice_coef: 0.8523
Epoch 380/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9773 - dice_coef: 0.9773 - val_loss: -0.8561 - val_dice_coef: 0.8561
Epoch 381/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9771 - dice_coef: 0.9771 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 382/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9769 - dice_coef: 0.9769 - val_loss: -0.8537 - val_dice_coef: 0.8537
Epoch 383/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9771 - dice_coef: 0.9771 - val_loss: -0.8525 - val_dice_coef: 0.8525
Epoch 384/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9776 - dice_coef: 0.9776 - val_loss: -0.8585 - val_dice_coef: 0.8585
Epoch 385/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9759 - dice_coef: 0.9759 - val_loss: -0.8303 - val_dice_coef: 0.8303
Epoch 386/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9588 - dice_coef: 0.9588 - val_loss: -0.8556 - val_dice_coef: 0.8556
Epoch 387/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9718 - dice_coef: 0.9718 - val_loss: -0.8491 - val_dice_coef: 0.8491
Epoch 388/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9756 - dice_coef: 0.9756 - val_loss: -0.8544 - val_dice_coef: 0.8544
Epoch 389/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9778 - dice_coef: 0.9778 - val_loss: -0.8550 - val_dice_coef: 0.8550
Epoch 390/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9761 - dice_coef: 0.9761 - val_loss: -0.8523 - val_dice_coef: 0.8523
Epoch 391/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9770 - dice_coef: 0.9770 - val_loss: -0.8529 - val_dice_coef: 0.8529
Epoch 392/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9786 - dice_coef: 0.9786 - val_loss: -0.8558 - val_dice_coef: 0.8558
Epoch 393/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9752 - dice_coef: 0.9752 - val_loss: -0.8559 - val_dice_coef: 0.8559
Epoch 394/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9733 - dice_coef: 0.9733 - val_loss: -0.8569 - val_dice_coef: 0.8569
Epoch 395/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9756 - dice_coef: 0.9756 - val_loss: -0.8525 - val_dice_coef: 0.8525
Epoch 396/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9776 - dice_coef: 0.9776 - val_loss: -0.8534 - val_dice_coef: 0.8534
Epoch 397/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9764 - dice_coef: 0.9764 - val_loss: -0.8563 - val_dice_coef: 0.8563
Epoch 398/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9768 - dice_coef: 0.9768 - val_loss: -0.8510 - val_dice_coef: 0.8510
Epoch 399/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9747 - dice_coef: 0.9747 - val_loss: -0.8577 - val_dice_coef: 0.8577
Epoch 400/400
1452/1452 [==============================] - 55s 38ms/step - loss: -0.9659 - dice_coef: 0.9659 - val_loss: -0.8552 - val_dice_coef: 0.8552
In [10]:
    # Save History
    f = open(dname_checkpoints + '/' + fname_history,'wb')
    pickle.dump(history.history,f)
    f.close
Out[10]:
<function BufferedWriter.close>
In [7]:
#
#  TEST MODE
#
mode = 'TEST'
if mode == "TEST":
    # Prediction (test) mode

    # 学習済みの重みをロード
    epoch = 200
    fname_weights = 'model_weights_%02d.h5'%(epoch)
    fpath_weights = os.path.join(dname_checkpoints, fname_weights)
    model_fcn01.load_weights(fpath_weights)
    print('==> done')
==> done
In [8]:
    # Read Test Data
    fnames = load_fnames('data/list_test_01.txt')

    [fpaths_xs_test,fpaths_ys_test] = make_fnames(fnames,'data/img','data/mask','OperatorA_')

    X_test = load_imgs_asarray(fpaths_xs_test, grayscale=False, target_size=target_size,
                                dim_ordering=dim_ordering)
  

    # トレーニング時に計算した平均・標準偏差をロード    
    print('loading mean and standard deviation from ' + fname_stats + '...')
    stats = np.load(dname_checkpoints + '/' + fname_stats)
    mean = stats['mean']
    std = stats['std']
    print('==> mean: ' + str(mean))
    print('==> std : ' + str(std))

    for i in range(3):
        X_test[:, i] = (X_test[:, i] - mean[i]) / std[i]
    print('==> done')
loading mean and standard deviation from stats01.npz...
==> mean: [130.65465  91.2685   76.63643]
==> std : [55.2817   43.990963 43.113483]
==> done
In [36]:
    from PIL import Image
    import matplotlib.pyplot as plt

    for ep in range(180,201,10):
        # 学習済みの重みをロード
        epoch = ep
        fname_weights = 'model_weights_%02d.h5'%(epoch)
        fpath_weights = os.path.join(dname_checkpoints, fname_weights)
        model_fcn01.load_weights(fpath_weights)
        print('==> done')

        # テストを開始
        outputs = model_fcn01.predict(X_test)
        #    outputs = model_fcn02.predict(X_test)

        # 出力を画像として保存
        dname_outputs = './outputs/'
        if not os.path.isdir(dname_outputs):
            print('create directory: %s'%(dname_outputs))
            os.mkdir(dname_outputs)

        print('saving outputs as images...')
        n = 0
        for i, array in enumerate(outputs):
            array = np.where(array > 0.5, 1, 0) # 二値に変換
            array = array.astype(np.float32)
            img_out = array_to_img(array, dim_ordering)
            # fpath_out = os.path.join(dname_outputs, fnames[i])
            fpath_out = os.path.join(dname_outputs, "%05d.png"%(n))
            img_out.save(fpath_out)
            n = n + 1

        print('==> done')

        n = 0
        diff = []
        dice_eval = []
        center_test = []
        center_gt = []

        for i in range(len(fpaths_xs_test)):
            # テスト画像
            im1 = Image.open(fpaths_xs_test[i])

            im1 = im1.resize((320,240)) 
            # 出力結果
            im2 = Image.open(os.path.join(dname_outputs, "%05d.png"%(n)))
            center_test.append(get_center(np.array(im2)))         
            im2 = im2.resize((320,240))
            # Grond Truth
            im3 = Image.open(fpaths_ys_test[i])
            im3t = im3.resize(target_size)
            center_gt.append(get_center(np.array(im3t)))
            im3 = im3.resize((320,240))

            im2_d = np.zeros((240,320,3), 'uint8')
            im2_d[:,:,0] = np.array(im2)
            im2_d[:,:,1] = np.array(im3)*255
            im2_d[:,:,2] = 0

            # Compute dice coeff
            im2a = np.array(im2)
            im2a[im2a > 0] = 1
            im3a = np.array(im3)
            im3a[im3a > 0] = 1

            overlap_a = np.array(im2a) * np.array(im3a)
            overlap_b = np.array(im2a) + np.array(im3a)
            #print('%03d: Dice Coeff = %f'%(i, 2*sum(sum(overlap_a))/sum(sum(overlap_b))))
            #print('%f'%img_dice_coeff(im2,im3))
            dice_eval.append(2*sum(sum(overlap_a))/sum(sum(overlap_b)))
            
            #plt.imshow(np.hstack((np.array(im1),np.array(im2_d))))
            #plt.show()
            if (i%100) == 0:
                print('Prcessing image %d / %d'%(i,len(fpaths_xs_test)))

            n = n + 1

        diff = np.array(center_test) - np.array(center_gt)
        print('%d: Dice eval av. : %f'%(ep,np.mean(np.array(dice_eval))))
        print('%d: Center Diff av. : %f'%(ep,np.sum(np.linalg.norm(diff,axis=1))/diff.shape[0]))
==> done
saving outputs as images...
==> done
Prcessing image 0 / 270
Prcessing image 100 / 270
Prcessing image 200 / 270
180: Dice eval av. : 0.789290
180: Center Diff av. : 4.956093
==> done
saving outputs as images...
==> done
Prcessing image 0 / 270
Prcessing image 100 / 270
Prcessing image 200 / 270
190: Dice eval av. : 0.775716
190: Center Diff av. : 5.326298
==> done
saving outputs as images...
==> done
Prcessing image 0 / 270
Prcessing image 100 / 270
Prcessing image 200 / 270
200: Dice eval av. : 0.776370
200: Center Diff av. : 5.625541
In [ ]:
    # テストを開始
    outputs = model_fcn01.predict(X_test)
#    outputs = model_fcn02.predict(X_test)
    
In [17]:
    # 出力を画像として保存
    dname_outputs = './outputs/'
    if not os.path.isdir(dname_outputs):
        print('create directory: %s'%(dname_outputs))
        os.mkdir(dname_outputs)

    print('saving outputs as images...')
    n = 0
    for i, array in enumerate(outputs):
        array = np.where(array > 0.5, 1, 0) # 二値に変換
        array = array.astype(np.float32)
        img_out = array_to_img(array, dim_ordering)
        # fpath_out = os.path.join(dname_outputs, fnames[i])
        fpath_out = os.path.join(dname_outputs, "%05d.png"%(n))
        img_out.save(fpath_out)
        n = n + 1

    print('==> done')
saving outputs as images...
==> done
In [21]:
    from PIL import Image
    import matplotlib.pyplot as plt

    n = 0
    dice_eval = []
    
    for i in range(len(fpaths_xs_test)):
        # テスト画像
        im1 = Image.open(fpaths_xs_test[i])
        im1 = im1.resize((320,240)) 
        # 出力結果
        im2 = Image.open(os.path.join(dname_outputs, "%05d.png"%(n)))
        im2 = im2.resize((320,240))
        # Grond Truth
        im3 = Image.open(fpaths_ys_test[i])
        im3 = im3.resize((320,240))

        im2_d = np.zeros((240,320,3), 'uint8')
        im2_d[:,:,0] = np.array(im2)
        im2_d[:,:,1] = np.array(im3)*255
        im2_d[:,:,2] = 0

        # Compute dice coeff
        im2a = np.array(im2)
        im2a[im2a > 0] = 1
        im3a = np.array(im3)
        im3a[im3a > 0] = 1
        
        overlap_a = np.array(im2a) * np.array(im3a)
        overlap_b = np.array(im2a) + np.array(im3a)
        print('%03d: Dice Coeff = %f'%(i, 2*sum(sum(overlap_a))/sum(sum(overlap_b))))
        print('%f'%img_dice_coeff(im2,im3))
        dice_eval.append(2*sum(sum(overlap_a))/sum(sum(overlap_b)))

        plt.imshow(np.hstack((np.array(im1),np.array(im2_d))))
        plt.show()

        n = n + 1
    
    print('Dice eval av. : %f'%np.mean(np.array(dice_eval)))
000: Dice Coeff = 0.899851
0.899851
001: Dice Coeff = 0.738682
0.738682
002: Dice Coeff = 0.869748
0.869748
003: Dice Coeff = 0.820842
0.820842
004: Dice Coeff = 0.729443
0.729443
005: Dice Coeff = 0.766404
0.766404
006: Dice Coeff = 0.766885
0.766885
007: Dice Coeff = 0.752830
0.752830
008: Dice Coeff = 0.870311
0.870311
009: Dice Coeff = 0.859688
0.859688
010: Dice Coeff = 0.926719
0.926719
011: Dice Coeff = 0.555901
0.555901
012: Dice Coeff = 0.818182
0.818182
013: Dice Coeff = 0.960317
0.960317
014: Dice Coeff = 0.635920
0.635920
015: Dice Coeff = 0.865707
0.865707
016: Dice Coeff = 0.874627
0.874627
017: Dice Coeff = 0.921438
0.921438
018: Dice Coeff = 0.899204
0.899204
019: Dice Coeff = 0.903614
0.903614
020: Dice Coeff = 0.876738
0.876738
021: Dice Coeff = 0.789474
0.789474
022: Dice Coeff = 0.882250
0.882250
023: Dice Coeff = 0.968654
0.968654
024: Dice Coeff = 0.958386
0.958386
025: Dice Coeff = 0.909297
0.909297
026: Dice Coeff = 0.944810
0.944810
027: Dice Coeff = 0.905051
0.905051
028: Dice Coeff = 0.932961
0.932961
029: Dice Coeff = 0.844391
0.844391
030: Dice Coeff = 0.933333
0.933333
031: Dice Coeff = 0.956618
0.956618
032: Dice Coeff = 0.894091
0.894091
033: Dice Coeff = 0.804598
0.804598
034: Dice Coeff = 0.942209
0.942209
035: Dice Coeff = 0.890715
0.890715
036: Dice Coeff = 0.874743
0.874743
037: Dice Coeff = 0.845369
0.845369
038: Dice Coeff = 0.751445
0.751445
039: Dice Coeff = 0.852564
0.852564
040: Dice Coeff = 0.873700
0.873700
041: Dice Coeff = 0.942714
0.942714
042: Dice Coeff = 0.926499
0.926499
043: Dice Coeff = 0.841584
0.841584
044: Dice Coeff = 0.887439
0.887439
045: Dice Coeff = 0.861518
0.861518
046: Dice Coeff = 0.924242
0.924242
047: Dice Coeff = 0.958018
0.958018
048: Dice Coeff = 0.893004
0.893004
049: Dice Coeff = 0.790607
0.790607
050: Dice Coeff = 0.888889
0.888889
051: Dice Coeff = 0.855377
0.855377
052: Dice Coeff = 0.939457
0.939457
053: Dice Coeff = 0.934579
0.934579
054: Dice Coeff = 0.925307
0.925307
055: Dice Coeff = 0.756303
0.756303
056: Dice Coeff = 0.915646
0.915646
057: Dice Coeff = 0.866477
0.866477
058: Dice Coeff = 0.923864
0.923864
059: Dice Coeff = 0.937557
0.937557
060: Dice Coeff = 0.869707
0.869707
061: Dice Coeff = 0.891599
0.891599
062: Dice Coeff = 0.882658
0.882658
063: Dice Coeff = 0.852941
0.852941
064: Dice Coeff = 0.772016
0.772016
065: Dice Coeff = 0.883146
0.883146
066: Dice Coeff = 0.777969
0.777969
067: Dice Coeff = 0.877657
0.877657
068: Dice Coeff = 0.890835
0.890835
069: Dice Coeff = 0.656906
0.656906
070: Dice Coeff = 0.847571
0.847571
071: Dice Coeff = 0.886680
0.886680
072: Dice Coeff = 0.879808
0.879808
073: Dice Coeff = 0.919774
0.919774
074: Dice Coeff = 0.883249
0.883249
075: Dice Coeff = 0.883227
0.883227
076: Dice Coeff = 0.756129
0.756129
077: Dice Coeff = 0.776952
0.776952
078: Dice Coeff = 0.723455
0.723455
079: Dice Coeff = 0.859091
0.859091
080: Dice Coeff = 0.799847
0.799847
081: Dice Coeff = 0.664356
0.664356
082: Dice Coeff = 0.594912
0.594912
083: Dice Coeff = 0.871935
0.871935
084: Dice Coeff = 0.805556
0.805556
085: Dice Coeff = 0.825137
0.825137
086: Dice Coeff = 0.765543
0.765543
087: Dice Coeff = 0.671551
0.671551
088: Dice Coeff = 0.910369
0.910369
089: Dice Coeff = 0.857790
0.857790
090: Dice Coeff = 0.822238
0.822238
091: Dice Coeff = 0.907486
0.907486
092: Dice Coeff = 0.930616
0.930616
093: Dice Coeff = 0.809422
0.809422
094: Dice Coeff = 0.919271
0.919271
095: Dice Coeff = 0.619883
0.619883
096: Dice Coeff = 0.788000
0.788000
097: Dice Coeff = 0.865072
0.865072
098: Dice Coeff = 0.825737
0.825737
099: Dice Coeff = 0.795556
0.795556
100: Dice Coeff = 0.784409
0.784409
101: Dice Coeff = 0.698639
0.698639
102: Dice Coeff = 0.607725
0.607725
103: Dice Coeff = 0.802834
0.802834
104: Dice Coeff = 0.651828
0.651828
105: Dice Coeff = 0.747145
0.747145
106: Dice Coeff = 0.665789
0.665789
107: Dice Coeff = 0.838870
0.838870
108: Dice Coeff = 0.751890
0.751890
109: Dice Coeff = 0.667461
0.667461
110: Dice Coeff = 0.823529
0.823529
111: Dice Coeff = 0.896209
0.896209
112: Dice Coeff = 0.756225
0.756225
113: Dice Coeff = 0.931507
0.931507
114: Dice Coeff = 0.905356
0.905356
115: Dice Coeff = 0.559767
0.559767
116: Dice Coeff = 0.899866
0.899866
117: Dice Coeff = 0.890667
0.890667
118: Dice Coeff = 0.883295
0.883295
119: Dice Coeff = 0.944444
0.944444
120: Dice Coeff = 0.887139
0.887139
121: Dice Coeff = 0.796380
0.796380
122: Dice Coeff = 0.890019
0.890019
123: Dice Coeff = 0.879855
0.879855
124: Dice Coeff = 0.856378
0.856378
125: Dice Coeff = 0.817043
0.817043
126: Dice Coeff = 0.729483
0.729483
127: Dice Coeff = 0.921273
0.921273
128: Dice Coeff = 0.640100
0.640100
129: Dice Coeff = 0.840090
0.840090
130: Dice Coeff = 0.863362
0.863362
131: Dice Coeff = 0.898907
0.898907
132: Dice Coeff = 0.917836
0.917836
133: Dice Coeff = 0.874610
0.874610
134: Dice Coeff = 0.783547
0.783547
135: Dice Coeff = 0.844195
0.844195
136: Dice Coeff = 0.899041
0.899041
137: Dice Coeff = 0.080560
0.080560
138: Dice Coeff = 0.946648
0.946648
139: Dice Coeff = 0.876344
0.876344
140: Dice Coeff = 0.911796
0.911796
141: Dice Coeff = 0.456825
0.456825
142: Dice Coeff = 0.739935
0.739935
143: Dice Coeff = 0.872727
0.872727
144: Dice Coeff = 0.883632
0.883632
145: Dice Coeff = 0.875385
0.875385
146: Dice Coeff = 0.849244
0.849244
147: Dice Coeff = 0.927921
0.927921
148: Dice Coeff = 0.823117
0.823117
149: Dice Coeff = 0.830075
0.830075
150: Dice Coeff = 0.808717
0.808717
151: Dice Coeff = 0.762510
0.762510
152: Dice Coeff = 0.823529
0.823529
153: Dice Coeff = 0.624733
0.624733
154: Dice Coeff = 0.869369
0.869369
155: Dice Coeff = 0.775606
0.775606
156: Dice Coeff = 0.815890
0.815890
157: Dice Coeff = 0.648199
0.648199
158: Dice Coeff = 0.916843
0.916843
159: Dice Coeff = 0.723898
0.723898
160: Dice Coeff = 0.788413
0.788413
161: Dice Coeff = 0.740883
0.740883
162: Dice Coeff = 0.879640
0.879640
163: Dice Coeff = 0.857143
0.857143
164: Dice Coeff = 0.772983
0.772983
165: Dice Coeff = 0.609808
0.609808
166: Dice Coeff = 0.841823
0.841823
167: Dice Coeff = 0.828135
0.828135
168: Dice Coeff = 0.757489
0.757489
169: Dice Coeff = 0.848546
0.848546
170: Dice Coeff = 0.669903
0.669903
171: Dice Coeff = 0.778281
0.778281
172: Dice Coeff = 0.780220
0.780220
173: Dice Coeff = 0.854902
0.854902
174: Dice Coeff = 0.905547
0.905547
175: Dice Coeff = 0.856525
0.856525
176: Dice Coeff = 0.748428
0.748428
177: Dice Coeff = 0.894376
0.894376
178: Dice Coeff = 0.796584
0.796584
179: Dice Coeff = 0.806826
0.806826
180: Dice Coeff = 0.807773
0.807773
181: Dice Coeff = 0.865385
0.865385
182: Dice Coeff = 0.736739
0.736739
183: Dice Coeff = 0.535211
0.535211
184: Dice Coeff = 0.782145
0.782145
185: Dice Coeff = 0.839339
0.839339
186: Dice Coeff = 0.720000
0.720000
187: Dice Coeff = 0.696219
0.696219
188: Dice Coeff = 0.833834
0.833834
189: Dice Coeff = 0.768212
0.768212
190: Dice Coeff = 0.698669
0.698669
191: Dice Coeff = 0.805643
0.805643
192: Dice Coeff = 0.350140
0.350140
193: Dice Coeff = 0.785789
0.785789
194: Dice Coeff = 0.336066
0.336066
195: Dice Coeff = 0.717557
0.717557
196: Dice Coeff = 0.838095
0.838095
197: Dice Coeff = 0.779968
0.779968
198: Dice Coeff = 0.843900
0.843900
199: Dice Coeff = 0.854421
0.854421
200: Dice Coeff = 0.857510
0.857510
201: Dice Coeff = 0.818452
0.818452
202: Dice Coeff = 0.657277
0.657277
203: Dice Coeff = 0.829305
0.829305
204: Dice Coeff = 0.772000
0.772000
205: Dice Coeff = 0.904239
0.904239
206: Dice Coeff = 0.875817
0.875817
207: Dice Coeff = 0.931891
0.931891
208: Dice Coeff = 0.802521
0.802521
209: Dice Coeff = 0.898026
0.898026
210: Dice Coeff = 0.909520
0.909520
211: Dice Coeff = 0.942316
0.942316
212: Dice Coeff = 0.757692
0.757692
213: Dice Coeff = 0.798403
0.798403
214: Dice Coeff = 0.837675
0.837675
215: Dice Coeff = 0.933468
0.933468
216: Dice Coeff = 0.720497
0.720497
217: Dice Coeff = 0.804401
0.804401
218: Dice Coeff = 0.821951
0.821951
219: Dice Coeff = 0.905035
0.905035
220: Dice Coeff = 0.850649
0.850649
221: Dice Coeff = 0.898236
0.898236
222: Dice Coeff = 0.781145
0.781145
223: Dice Coeff = 0.870990
0.870990
224: Dice Coeff = 0.767947
0.767947
225: Dice Coeff = 0.845390
0.845390
226: Dice Coeff = 0.757372
0.757372
227: Dice Coeff = 0.867446
0.867446
228: Dice Coeff = 0.744257
0.744257
229: Dice Coeff = 0.848580
0.848580
230: Dice Coeff = 0.869425
0.869425
231: Dice Coeff = 0.942931
0.942931
232: Dice Coeff = 0.694387
0.694387
233: Dice Coeff = 0.807080
0.807080
234: Dice Coeff = 0.866787
0.866787
235: Dice Coeff = 0.927265
0.927265
236: Dice Coeff = 0.710480
0.710480
237: Dice Coeff = 0.622500
0.622500
238: Dice Coeff = 0.867442
0.867442
239: Dice Coeff = 0.897426
0.897426
240: Dice Coeff = 0.855754
0.855754
241: Dice Coeff = 0.587838
0.587838
242: Dice Coeff = 0.639437
0.639437
243: Dice Coeff = 0.860733
0.860733
244: Dice Coeff = 0.885167
0.885167
245: Dice Coeff = 0.934150
0.934150
246: Dice Coeff = 0.884163
0.884163
247: Dice Coeff = 0.900148
0.900148
248: Dice Coeff = 0.880597
0.880597
249: Dice Coeff = 0.833333
0.833333
250: Dice Coeff = 0.811944
0.811944
251: Dice Coeff = 0.834050
0.834050
252: Dice Coeff = 0.823308
0.823308
253: Dice Coeff = 0.772932
0.772932
254: Dice Coeff = 0.797896
0.797896
255: Dice Coeff = 0.692191
0.692191
256: Dice Coeff = 0.801782
0.801782
257: Dice Coeff = 0.910638
0.910638
258: Dice Coeff = 0.642100
0.642100
259: Dice Coeff = 0.867635
0.867635
260: Dice Coeff = 0.560113
0.560113
261: Dice Coeff = 0.658397
0.658397
262: Dice Coeff = 0.760504
0.760504
263: Dice Coeff = 0.772215
0.772215
264: Dice Coeff = 0.517572
0.517572
265: Dice Coeff = 0.831606
0.831606
266: Dice Coeff = 0.660652
0.660652
267: Dice Coeff = 0.758245
0.758245
268: Dice Coeff = 0.360577
0.360577
269: Dice Coeff = 0.896346
0.896346
Dice eval av. : 0.813215
In [22]:
#
#   Show History
#
mode = "SHOW_HISTORY"
if mode == "SHOW_HISTORY":
    # load pickle
    print(dname_checkpoints + '/' + fname_history)
    history = pickle.load(open(dname_checkpoints + '/' + fname_history, 'rb'))
    
    for k in history.keys():
        plt.plot(history[k])
        plt.title(k)
        plt.show()
checkpoints_fcn01/history.pkl